# Ray 0.8.1

- Product: Ray (https://whatsnew.fyi/product/ray)
- Vendor: Anyscale
- Date: 2020-01-27
- Version: 0.8.1
- Original notes: https://github.com/ray-project/ray/releases/tag/ray-0.8.1
- Permalink: https://whatsnew.fyi/product/ray/releases/0.8.1

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- **added** — ObjectIDs corresponding to ray.put() objects and task returns are now reference counted locally in Python and when passed into a remote task as an argument
- **added** — Actors can now define async def methods and Ray will run multiple method invocations in the same event loop, with maximum concurrency level adjustable via ActorClass.options(max_concurrency=N).remote()
- **added** — Ray ObjectIDs can now be directly awaited using the Python API with await my_object_id, and can be converted to asyncio.Future using ObjectID.as_future()
- **added** — Added experimental parallel iterators API with ParallelIterator for conveniently loading and processing data into Ray actors
- **added** — Added multiprocessing.Pool API support to scale existing programs from a single node to a cluster by changing only the import statement
- **deprecated** — Python 2 support is deprecated
- **fixed** — Fixed bug when failing to import remote functions or actors with args and kwargs
- **added** — Added actor.__ray_kill__() to terminate actors immediately
- **added** — Added 'ray stat' command for debugging
- **added** — Added documentation for fault tolerance behavior
- **changed** — Treat static methods as class methods instead of instance methods in actors
- **changed** — Redis now binds to localhost and has a password set by default
- **changed** — Changed foreach_policy to foreach_trainable_policy in RLlib
- **added** — Get checkpoints paths for a trial after tuning in Tune
- **added** — Added async restores and S3/GCP-capable trial fault tolerance in Tune
- **added** — Added support for Type Hinting for Python 3 in Tune
- **added** — Added pluggable queueing policy for Ray Serve
- **added** — Added BackendConfig for Ray Serve
- **added** — Added fault tolerance support for PyTorch in Ray SGD

Ray 0.8.1 Release Notes
=======================

Highlights
----------
- `ObjectID`s corresponding to `ray.put()` objects and task returns are now reference counted locally in Python and when passed into a remote task as an argument. `ObjectID`s that have a nonzero reference count will not be evicted from the object store. Note that references for `ObjectID`s passed into remote tasks inside of other objects (e.g., `f.remote((ObjectID,))` or `f.remote([ObjectID])`) are not currently accounted for. (#6554)
- `asyncio` actor support: actors can now define `async def` method and Ray will run multiple method invocations in the same event loop. The maximum concurrency level can be adjusted with `ActorClass.options(max_concurrency=2000).remote()`.
- `asyncio` `ObjectID` support: Ray ObjectIDs can now be directly awaited using the Python API. `await my_object_id` is similar to `ray.get(my_object_id)`, but allows context switching to make the operation non-blocking. You can also convert an `ObjectID` to a `asyncio.Future` using `ObjectID.as_future()`.
- Added experimental parallel iterators API (#6644, #6726): `ParallelIterator`s can be used to more convienently load and process data into Ray actors. See the [documentation](https://ray.readthedocs.io/en/latest/iter.html) for details.
- Added multiprocessing.Pool API (#6194): Ray now supports the `multiprocessing.Pool` API out of the box, so you can scale existing programs up from a single node to a cluster by only changing the import statment. See the [documentation](https://ray.readthedocs.io/en/latest/multiprocessing.html) for details.

Core
----
- Deprecated Python 2 (#6581, #6601, #6624, #6665)
- Fixed bug when failing to import remote functions or actors with args and kwargs (#6577)
- Many improvements to the dashboard (#6493, #6516, #6521, #6574, #6590, #6652, #6671, #6683, #6810)
- Progress towards Windows compatibility (#6446, #6548, #6653, #6706)
- Redis now binds to localhost and has a password set by default (#6481)
- Added `actor.__ray_kill__()` to terminate actors immediately (#6523)
- Added 'ray stat' command for debugging (#6622)
- Added documentation for fault tolerance behavior (#6698)
- Treat static methods as class methods instead of instance methods in actors (#6756)

RLlib
-----
- DQN distributional model: Replace all legacy tf.contrib imports with tf.keras.layers.xyz or tf.initializers.xyz (#6772)
- SAC site changes (#6759)
- PG unify/cleanup tf vs torch and PG functionality test cases (tf + torch) (#6650)
- SAC for Mujoco Environments (#6642)
- Tuple action dist tensors not reduced properly in eager mode (#6615)
- Changed foreach_policy to foreach_trainable_policy (#6564)
- Wrapper for the dm_env interface (#6468)

Tune
----
- Get checkpoints paths for a trial after tuning (#6643)
- Async restores and S3/GCP-capable trial FT (#6376)
- Usability errors PBT (#5972)
- Demo exporting trained models in pbt examples (#6533)
- Avoid duplication in TrialRunner execution (#6598)
- Update params for optimizer in reset_config (#6522)
- Support Type Hinting for py3 (#6571)

Other Libraries
---------------
- [serve] Pluggable Queueing Policy (#6492)
- [serve] Added BackendConfig (#6541)
- [sgd] Fault tolerance support for pytorch + revamp documentation (#6465)

Thanks
-------

We thank the following contributors for their work on this release:

@chaokunyang, @Qstar, @simon-mo, @wlx65003, @stephanie-wang, @alindkhare, @ashione, @harrisonfeng, @JingGe, @pcmoritz, @zhijunfu, @BalaBalaYi, @kfstorm, @richardliaw, @mitchellstern, @michaelzhiluo, @ziyadedher, @istoica, @EyalSel, @ffbin, @raulchen, @edoakes, @chenk008, @frthjf, @mslapek, @gehring, @hhbyyh, @zzyunzhi, @zhu-eric, @MissiontoMars, @sven1977, @walterddr, @micafan, @inventormc, @robertnishihara, @ericl, @ZhongxiaYan, @mehrdadn, @jovany-wang, @ujvl, @bharatpn